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miWQS 0.4.4

• Edited the Description field of DESCRIPTION file to meet CRAN standards.

miWQS 0.4.3

• Updated the email address of the maintainer in DESCRIPTION.
• make.quantile.matrix(): Adjusted quantile function by adding optional argument digits = 7 to reflect change in quantile version in CRAN version 4.0.5. The change does not affect CRAN version 4.0.4 or below.

The names of quantile()’s result no longer depend on the global getOption(“digits”), but quantile() gets a new optional argument digits = 7 instead.

miWQS 0.4.2

• Fixed discrepancy in accordance with CRAN standards: Removed global variables estimate and chemical from environment in plot.wqs().

miWQS 0.4.1

• Fixed discrepancies with CRAN standards:
• DESCRIPTION FILE: Rewrote dois to match CRAN standards (<doi:…>, not <:…>)
• impute.multivariate.bayesian(): Removed empty details section

miWQS 0.4.0 – TO CRAN

Breaking Changes

• Additional Resource: A methods paper (Hargarten & Wheeler (2020) <:10.1016/j.envres.2020.109466>) is cited in the README, DESCRIPTION, and some function files. This paper provides theoretical details in using the package.
• estimate.wqs(): Can now compare training and validation datasets, which is commonly done in WQS analysis.
• plot.wqs(): Removed defunct filename argument. Plots are no longer saved automatically; please save manually using ().

New Functions

• New impute.multivariate.regress(): imputes all chemicals jointly using a multivariate Bayesian regression.

Minor Updates

• Documentation clarity and edits
• do.many.wqs():
  • Fixed error in collecting WQS results. When training proportion not 1, WQS is smaller than matrix to hold values.
  • Added a message that included the sample size, number of chemicals, datasets to impute, and number of covariates modelled.
  • Passed the explicit B argument to the ... argument. Should not impact any code, as it is called within estimate.wqs().
• estimate.wqs():
  • Cleaned up code
  • Updated accessories summarize.compare() and make.descriptive.tables().
• impute.sub(): replaced for-loop with much faster tidyr::replace_na().
• impute.boot() and impute.Lubin(): Now uses the survival routines that have been updated in version 3.
• plot.wqs():
  • Now uses tidyr::pivot_longer() instead of tidyr::gather() in response to update in tidyr package from v. 0.8 to v. 1.0.0. See vignette("pivot") and vignette("in-packages")in tidyr documentation for more information.
  • Updated documentation to require tidyr to be v. 1.0.0.
    
  • Added a ggplot2 layer to make x-axis smaller so you can read labels on weights histogram
• Internal Improvements
  • accessory check_constants(): Fixed bug that returns error if the number of bootstraps (B), imputed datasets (K), or length of chain (T) is 0
  • new accessory imp_cond_MVN: An accessory function used to impute conditional truncated multivariate normal used in impute.multivariate.regress(). Added condMVNorm & tmvtnorm packages to DESCRIPTION.
  • accessory formatMedianIQR: Changed format of median and IQR to “median[Q1, Q3]”.
  • accessory is.even(), is.odd() – now accepts tolerance argument for consistency with other is... functions.
  • accessory specify.init(): change argument to C.
  • accessory replace_na(): Changes made in impute.sub()

miWQS 0.2.0

New Features

• New estimate.wqs.formula() now has a formula capability. Can type in a full formula and specify column names instead of dividing up the data into three columns.
• impute.Lubin():
  • Softly deprecated. Use impute.boot instead.
  • If chemcol is complete, the function NOW returns complete data with warning
  • The bootstrap_index element is now factors instead of numbers. Easier to see what subjects were selected.
• pool.mi():
  • Added argument prt to print to standard output so that the pool.mi object can be read in an understandable fashion.
  • Argument methods is NOW more robust by adding tolower(). Now, if someone writes in all caps, the function still works.
  • Accessory mice.df() is now clearer – only accepts either method to avoid mistake.

Minor Improvements and Changes

• Documentation & example clarity.
• Moved the base R packages from IMPORTS to DEPENDS for clarity. As the base R packages are already attached, this should have no impact on package performance.
• Replace all checks using class(.)==matrix with is(), as directed from CRAN.
• Clarified internal performance
  • accessory check_constants(): Used sprintf() for more generic error returns.
  • accessory check_imputation: Removed checking Z here. Now Z is checked in each impute... function.
  • accessory head.array(): made package for head() clear by using utils::head().
  • accessory is.even(), is.odd() – now accepts tolerance argument for consistency with other is... functions.
• combine.AIC(): removed comma from output that was generated from format.mean.sd() (Added July 22, 2019)
• estimate.wqs():
  • Syntax clarification,
  • Added if/else on signal function options for speed.
  • Now uses check_wqs_function(); deleted duplicate check_function() from before.
• impute.boot():
  • Added note to contact Jay Lubin for SAS macro
  • Cleaned up code/spaces. Didn’t change functionality.

miWQS 0.1.0

Breaking changes

• The plot.wqs() no longer automatically saves the plots to reduce clutter and save the user’s workspace. However, you can still use ggsave() on the output if you wish to save WQS plots. If you depended on this behavior, you’ll need to condition on packageVersion("miWQS") > "0.0.0".
• The title of the package is changed from “analysis” to “regression” to make the name more specific.
• Creates a README vignette.
• Shortens description in DESCRIPTION file for clarity.

New Features

• New analyze.individually() does individual chemical analysis. The outcome is independently regressed on each chemical. Individual chemical analyses with the outcome can be used to determine whether the mixture of chemicals is positively or negatively related to the outcome (the b1.pos argument in estimate.wqs()).
• New do.many.wqs() does many WQS analyses, which is useful in the second stage of multiple imputation.
• New combine.AIC() combines AIC results from many WQS analyses, similar in spirit to pool.mi().
• New impute.boot() performs bootstrapping imputation for many chemicals, not just one.
• New impute.sub() imputes the values below the detection limit with 1/sqrt(2) of that’s chemical’s detection limit.

Minor improvements and fixes

• Documentation is clarified and keywords are added.
• Most cat() used in the functions are changed to messages message() so that the user can suppress messages using suppressMessage().
• Consistent comments in all functions now use notation #>.
• The estimate.wqs()
  • Arguments
    • B argument: Documentation is clarified when bootstrapping within WQS. You now can do WQS regression without bootstrapping, but it is not recommended.
    • place.bdls.in.Q1 argument now does something. You can set it to FALSE and regular quantiles are made. Passed to make.quantile.matrix().
    • offset argument fixes transfer to glm2(). User should enter the offset on the normal scale; the logarithm is not taken. It is passed to glm2() and glm(). FROM: The offset argument in glm2() has a default value of 0. The offset value in estimate.wqs() by default is a vector of 1’s. NOW: When using glm2(), offset argument now takes the logarithm as expected in all instances, (especially in wqs.fit()). User does not change the default of offset value.
  • Inner Mechanics
    • Removes duplicate output in by adding the suppressMessages() function.
    • Uses wqs.fit() accessory function instead of code in estimate.wqs().
    • Changes lower case “c” to upper case “C” to avoid conflict with c().
• The impute.univariate.bayesian.mi() function:
  • T argument: changes default length of chain to 1,000 in order to be consistent with other functions.
  • indicator.miss output now returns as a single number (a sum) rather than a vector.
  • Fixes bug in initial values for the standard deviation in MCMC chain. Calculates standard deviation using the logarithm of cov() function. FROM: The complete.observations argument was used for observed X. NOW: standard deviations are calculated based on the substituted imputed chemical matrix, X, as covariances may not exist if X has many missing values.
  • Fixes bug so that the imputed values now draw from the last state, instead of the second-to-last state.
  • Inner Mechanics
    • Reduced # of objects to be used in finding initial2.
    • Changed object name x.miss.initial to log.x.miss.initial.
    • Examples still remain the same.
• The make.quantile.matrix():
  • Adds the place.bdls.in.Q1 argument. The default automatically places any missing values in X placed into the first quantile. We suggest the user does not specify this argument unless the user wants to be specific in how missing values in components are handled. In version 0.0.9, this argument previously had no effect on how quantiles were made. This argument now has an effect.
  • Fixes an error if there are ties in the quantiles. An error occurred in cut.default(...): 'breaks' are not unique. We use the .bincode() function instead so that ties are handled if they arise.
• The print.wqs() now concatenates, instead of prints, the convergence of the bootstrap samples.
• The simdata87 data:
  • element Z contains renamed covariate names for clarity.
  • elements X.true, X.bdl, DL, delta, n0: all chemical names are converted to plain text for clarity to be used in R. The “p-p`” are removed, and the “alpha” and “gamma” are spelled out directly.

miWQS 0.0.9

• First Release of Package to the public.
• For updates to CRAN team, see cran-comments.
• Replaces examples using example dataset in package instead of using package wqs. Looks cleaner
• Removes printed output from estimate.wqs().
• Makes documentation from estimate.wqs() clearer.
• Cleans print.wqs() documentation

miWQS 0.0.8

• Reworked plot.wqs() function by using ggplot2 instead of base plotting in R.

miWQS 0.0.7

• Fixes bug in doing Poisson Rate WQS regressions. Adds argument offset to the check_function() and randomize.train().
• For updates to CRAN team, see cran-comments.

miWQS 0.0.0

• Adds a NEWS.md file to track changes to the package.
• First Release of the Package to CRAN team
• Successfully passed windows check.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.